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                      <h5 class="mt-0">k近邻算法</h5>
                      KNN是通过测量不同特征值之间的距离进行分类。它的思路是：如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别，则该样本也属于这个类别，其中K通常是不大于20的整数。
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                      <h5 class="mt-0">朴素贝叶斯算法</h5>
                      在了解朴素贝叶斯的算法之前，我们需要对相关必须的统计学知识做一个回顾。贝叶斯学派的思想可以概括为先验概率+数据=后验概率。也就是说我们在实际问题中需要得到的后验概率，可以通过先验概率和数据一起综合得到。
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                      <h5 class="mt-0">决策树</h5>
                      决策树学习的算法通常是一个递归地选择最优特征，并根据该特征对训练数据集进行分割，使得对各个子数据集有一个最好的分类过程，这一过程对应着对特征空间的划分，也对应着决策树的构建。
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